Understanding the nutritional content of food from visual data is a
challenging computer vision problem, with the potential to have a positive and
widespread impact on public health. Studies in this area are limited to
existing datasets in the field that lack sufficient diversity or labels
required for training models with nutritional understanding capability. We
introduce Nutrition5k, a novel dataset of 5k diverse, real world food dishes
with corresponding video streams, depth images, component weights, and high
accuracy nutritional content annotation. We demonstrate the potential of this
dataset by training a computer vision algorithm capable of predicting the
caloric and macronutrient values of a complex, real world dish at an accuracy
that outperforms professional nutritionists. Further we present a baseline for
incorporating depth sensor data to improve nutrition predictions. We will
publicly release Nutrition5k in the hope that it will accelerate innovation in
the space of nutritional understanding.

该研究通过引入 Nutrition5k 数据集，采用计算机视觉算法能够精确预测真实世界中的复杂菜肴的热量和营养素值，并且具有超过专业营养学家的准确性，使这一领域的营养理解得到加速创新。